REM Alarm Clock

1. Project Engineer: David Beighe Project Mentor: Dr. Muthuswamy Hypothesis Future Work Learning Objectives References Project Planning Impact Data Process Abstract Discussion Acknowledgements Introduction Analysis Results I would like to thank the following people: Dr. Muthuswamy for mentoring me the past two years and assisting me with this project Dr. Kodibagkar and Dr. Pizziconi for instructing the applied projects class Dr. LaBelle and Dr. Towe for being on my applied projects committee Difficulty waking up to an alarm clock is usually the result of waking from deep sleep, which results in grogginess, irritability, impaired motor skills and decreased cognitive ability. The goal of this device is to detect the stage of sleep and only wake the user from light sleep. The aim is to accomplish this by measuring eye movements with the use of an accelerometer, wirelessly transmitting the data, and processing the data so it can wake the user at a time that minimizes sleep inertia. This utilizes the principle of sleep stages and the cyclical nature of sleep. Ultimately, this device would be a rather simple, but accurate, monitor that can be made available at a low cost. When people have variable schedules between days or weeks, such as college students with different class schedules or shift workers with varying work schedules, they are often sleeping out of sync with their Circadian rhythm. This problem is partially remedied by an alarm clock, however it is possible that the alarm will sound during a stage of deep sleep. This would lead to grogginess in the morning, which can cause impaired cognitive and motor abilities after waking up. It is also more difficult to wake a person from deep sleep, so the person is less likely to hear the alarm and wake up. The goal of this device is to wake users from light sleep as close to their wakeup time as possible. Existing devices can monitor sleep stage from EEG signals and general body movement to accomplish the same thing, but they can be inconsistent and expensive. [1] B. Peters, “How does sleep inertia make it hard to wake up?” About.com Sleep, 2009 This device would use movement calculated from REM to figure out which stage of sleep the user is reasonably in. This is because REM arises (almost always) from light sleep and the user returns to light sleep after REM. By determining the patterns in the intervals at which REM occurs, it can reasonably be predicted when the user is in light sleep without using cues from body movement and EEG signals which can be inaccurate. • Much of the preliminary project planning was completed during the capstone project phase, in fall 2011 and spring 2012. As a result the project’s direction was already solidified and the preexisting prototype was simply being modified to better address the customer needs. Although the process was fairly iterative, here is a general timeline of the project: • January-February 2013: Plan hardware assembly and order parts • February-March 2013: Assemble/solder hardware • March-April 2013: Fine tune software and test device In addition to improving my general product design skills, I also hoped to improve my technical proficiency with hardware and software design, specifically for small microcomputer-based applications. One particular proficiency that wasn’t covered in any of my classes prior to this was building and programming devices based on wireless technologies. Lastly, as with any large project, I hoped to gain experience problem-solving and troubleshooting issues that would arise. If the device is going to be based on an accelerometer moving forward it will likely have to be more sensitive but less noisy, which will likely drive up the cost of the device. Alternative transducers should be tested for detecting eye movements, but that would require an expanded budget. In its current incarnation, the device could probably be reprogrammed to distinguish light sleep versus deep sleep, this may be able to work in conjunction with REM detection. Although the device seemed to perform well at times, the results of the test are not encouraging. The large variability of sleep cycle length suggests that the current process to distinguish REM stages from NREM stages is not very accurate and it is simply guessing at times. While the device itself works in that it monitors accelerometer data and wirelessly outputs the results to another computer, it currently seems like there is no known way to use this data to distinguish REM from NREM sleep. This is likely due to the noise from other motion of the body being on the same order as that of the random eye movements. This could be due to the accelerometer not being close enough to the eyelid during sleep, although the current design doesn’t allow it to be any closer than it already is. It’s also possible that the process is currently “looking” for the wrong conditions to be satisfied to determine REM sleep, and thus the raw data may need to be compared to a clinical polysomnograph. The chart below describes the main decision-making criterion for determining when the subject is in REM or NREM sleep. Basically if the subject registers a sufficient number of hits above the threshold of what constitutes an eye movement, then that minute is categorized as REM. If not it is categorized as NREM. The project was encouraging for my development as an engineer. I was able to build and program my first wireless microcontroller-based device. Despite the issues still present with the device, I feel I did a good job troubleshooting and improving the software. I feel more confident in my engineering abilities moving forward. Figure 3: Sleep cycle lengths and REM stage lengths for the modified testing method Figure 2: Sleep cycle lengths and REM stage lengths for the initial testing method The figures above show the average time of each sleep cycle along with the average time of each REM stage. The length of the REM stage has a fixed amount of variability, as the program will only look for likely REM stages in lengths of 10-30 minutes, as this is how long human REM stages tend to last. The test here was to see if the program was accurately monitoring sleep cycle length, as it should not vary much for the same person. The standard deviation of both sleep cycles and REM stages is graphed as the error bars around the average times for both. Because of the large variation in sleep cycle length it is likely that both protocols are flawed. It’s likely the initial test is detecting too many false positives and the modified test is missing too many REM stages. Data was collected over a period of two weeks. Although the data is displayed consecutively, it is purely for display purposes only. Not all data points were consecutive (taken during the same sleep session). Data was collected by measuring accelerometer data every second and recording the absolute difference between each second. These data were measured to see if they were above a threshold established through preliminary tests and the total number of data above threshold, along with the sum of the movement, was displayed for every minute so it could be analyzed. An algorithm was then run on the data to determine when the subject was in REM sleep, and from that data sleep cycle and REM stage length were determined. REM Alarm Clock Figure 5: Average sleep cycle lengths and REM stage lengths for the modified testing method Figure 4: Average sleep cycle lengths and REM stage lengths for the initial testing method School of Biological and Health Systems Engineering Ira A. Fulton School of EngineeringArizona State University Accelerometer Data Thresholded Majority <=2 Majority >2 REM[i] = 0 REM[i] = 1 Figure 6: The main device, consisting of an Arduino Pro microcontroller stacked with a wireless shield that allows the Arduino to communicate wirelessly with a computer through the blue-colored module on top. A battery is connected to power the device. The device is connected to an accelerometer embedded in a sleeping mask. Figure 1: Flow chart detailing the progress of work on the project